A Kind of Message-recoverable Fairness Blind Digital Signature Scheme

AbstractBlind digital signature indeed protects interests of the participants to some extent, but the anonymity of blind digital signature present exploit opportunities to attackers. Aiming at problems of current fairness blind digital signature schemes can not simultaneously guarantee completely fairness and can not recover message, the paper proposed a kind of message-recoverable fairness blind signature scheme and analyzed its correctness, security and fairness. The analysis results show that the just can authorize user's identity and correspond it to original signature message with this scheme, and the user can not forge fairness information of just

Pedestrian dynamics in single-file movement of crowd with different age compositions

An aging population is bringing new challenges to the management of escape routes and facility design in many countries. This paper investigates pedestrian movement properties of crowd with different age compositions. Three pedestrian groups are considered: young student group, old people group and mixed group. It is found that traffic jams occur more frequently in mixed group due to the great differences of mobilities and self-adaptive abilities among pedestrians. The jams propagate backward with a velocity 0.4 m/s for global density around 1.75 m-1 and 0.3 m/s for higher than 2.3 m-1. The fundamental diagrams of the three groups are obviously different from each other and cannot be unified into one diagram by direct non-dimensionalization. Unlike previous studies, three linear regimes in mixed group but only two regimes in young student group are observed in the headway-velocity relation, which is also verified in the fundamental diagram. Different ages and mobilities of pedestrians in a crowd cause the heterogeneity of system and influence the properties of pedestrian dynamics significantly. It indicates that the density is not the only factor leading to jams in pedestrian traffic. The composition of crowd has to be considered in understanding pedestrian dynamics and facility design.Comment: 11 pages, 13 figures, 3 table

AGAD: Adversarial Generative Anomaly Detection

Anomaly detection suffered from the lack of anomalies due to the diversity of abnormalities and the difficulties of obtaining large-scale anomaly data. Semi-supervised anomaly detection methods are often used to solely leverage normal data to detect abnormalities that deviated from the learnt normality distributions. Meanwhile, given the fact that limited anomaly data can be obtained with a minor cost in practice, some researches also investigated anomaly detection methods under supervised scenarios with limited anomaly data. In order to address the lack of abnormal data for robust anomaly detection, we propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based anomaly detection paradigm that learns to detect anomalies by generating \textit{contextual adversarial information} from the massive normal examples. Essentially, our method generates pseudo-anomaly data for both supervised and semi-supervised anomaly detection scenarios. Extensive experiments are carried out on multiple benchmark datasets and real-world datasets, the results show significant improvement in both supervised and semi-supervised scenarios. Importantly, our approach is data-efficient that can boost up the detection accuracy with no more than 5% anomalous training data

Latent Embeddings for Collective Activity Recognition

Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previ- ous state-of-the-art methods using hand-crafted potentials in conventional graphical model which can only define a limited range of relations. Thus, the complex structural de- pendencies among individuals involved in a collective sce- nario cannot be fully modeled. In this paper, we overcome these limitations by embedding latent variables into feature space and learning the feature mapping functions in a deep learning framework. The embeddings of latent variables build a global relation containing person-group interac- tions and richer contextual information by jointly modeling broader range of individuals. Besides, we assemble atten- tion mechanism during embedding for achieving more com- pact representations. We evaluate our method on three col- lective activity datasets, where we contribute a much larger dataset in this work. The proposed model has achieved clearly better performance as compared to the state-of-the- art methods in our experiments.Comment: 6pages, accepted by IEEE-AVSS201

Changes of pore structure and chloride content in cement pastes after pore solution expression

Pore solution expression is a widely accepted approach to extract pore solution of cement-based materials by appllying high pressure. In this study, the variations of pore solution distribution and chloride content in cement pastes before and after pore solution expression were examined. The results showed that the value of chloride concentration index N-c were mostly higher than 1.0 for cement pastes immersed in NaCl solution, and decreased with the chloride concentration of soaking solution and water-to-binder (w/b) ratio. During the pore solution expression, the pores larger than 40 nm were totally removed and the porosity of smaller pore was decreased. Based on a proposed physical model on structure of cement paste, the value of N-c was calculated according to the variations of pore structure and chloride content during pore solution expression. The calculated results showed similar trend as the experimental results obtained by pore solution expression method